{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import ExtraTreeRegressor\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import AdaBoostRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.ensemble import BaggingRegressor\n",
    "\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_absolute_error #平方绝对误差\n",
    "from sklearn.metrics import r2_score#R square\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "#显示所有列\n",
    "pd.set_option('display.max_columns', None)\n",
    "#显示所有行\n",
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_excel(\"数据.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品性质：硫含量</th>\n",
       "      <th>产品性质：辛烷值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.2</td>\n",
       "      <td>90.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.8</td>\n",
       "      <td>90.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.2</td>\n",
       "      <td>90.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.2</td>\n",
       "      <td>90.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.9</td>\n",
       "      <td>89.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.2</td>\n",
       "      <td>90.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3.2</td>\n",
       "      <td>90.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.1</td>\n",
       "      <td>90.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3.8</td>\n",
       "      <td>89.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.5</td>\n",
       "      <td>88.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.3</td>\n",
       "      <td>89.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3.7</td>\n",
       "      <td>89.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3.4</td>\n",
       "      <td>89.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3.2</td>\n",
       "      <td>89.69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    产品性质：硫含量  产品性质：辛烷值\n",
       "0        3.2     90.20\n",
       "1        3.8     90.20\n",
       "2        3.2     90.30\n",
       "3        3.2     90.10\n",
       "4        3.2     89.90\n",
       "5        3.2     89.70\n",
       "6        3.9     89.92\n",
       "7        3.2     90.42\n",
       "8        3.2     90.12\n",
       "9        4.1     90.12\n",
       "10       3.8     89.82\n",
       "11       3.2     89.72\n",
       "12       3.2     89.52\n",
       "13       4.5     88.92\n",
       "14       3.2     89.92\n",
       "15       3.2     89.20\n",
       "16       3.2     89.20\n",
       "17       3.2     89.50\n",
       "18       3.3     89.30\n",
       "19       3.7     89.72\n",
       "20       3.4     89.70\n",
       "21       3.2     89.76\n",
       "22       3.2     89.89\n",
       "23       3.2     89.39\n",
       "24       3.2     89.69"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "data['产品性质：辛烷值'].plot(label='产品性质：辛烷值')\n",
    "data['产品性质：硫含量'].plot(label='产品性质：硫含量', secondary_y=True)\n",
    "plt.xticks(range(0, 24))\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '变化轨迹.xlsx'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-54c2f264c0bc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"变化轨迹.xlsx\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\soft\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    186\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    186\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 188\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    189\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    190\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel.py\u001b[0m in \u001b[0;36mread_excel\u001b[1;34m(io, sheet_name, header, names, index_col, parse_cols, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, keep_default_na, verbose, parse_dates, date_parser, thousands, comment, skip_footer, skipfooter, convert_float, mangle_dupe_cols, **kwds)\u001b[0m\n\u001b[0;32m    348\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    349\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 350\u001b[1;33m         \u001b[0mio\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    351\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    352\u001b[0m     return io.parse(\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, io, engine)\u001b[0m\n\u001b[0;32m    651\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_io\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_stringify_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    652\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 653\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engines\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    654\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    655\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__fspath__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\lib\\site-packages\\pandas\\io\\excel.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, filepath_or_buffer)\u001b[0m\n\u001b[0;32m    422\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 424\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    425\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    426\u001b[0m             raise ValueError('Must explicitly set engine if not passing in'\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\lib\\site-packages\\xlrd\\__init__.py\u001b[0m in \u001b[0;36mopen_workbook\u001b[1;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[0;32m    109\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    110\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpanduser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 111\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"rb\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    112\u001b[0m             \u001b[0mpeek\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpeeksz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    113\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mpeek\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34mb\"PK\\x03\\x04\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# a ZIP file\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '变化轨迹.xlsx'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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